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- General field: Applicable to general objects, including but not limited to: human face, clothes, tools, animal, ...
- Fast generation: Achieve object customization within seconds, without any further fine-tuning.
- User friendly: Just one single reference image and a text prompt can meet the customization requirement.
- Outstanding results: Ensure high ID fidelity, flexible text editability and high quality.
- Various applications: Diverse applications such as general object customization, virtual try-on, ID-mixing, ...
AnyMaker consists of three crucial ID processing modules: General ID Extraction Module, Dual-Level ID Injection Module, ID-Aware Decoupling Module.
The AnyMaker exhibits outstanding capabilities of high-quality customization for general objects, and even beat task-specialized methods in the specific domains, such as human customization and virtual try-on, in terms of ID fidelity and text editability.
To promot the research of the general object customization, we construct the first large-scale general ID dataset, named as Multi-Category ID-Consistent (MC-IDC) dataset. Our dataset consists of approximately 315,000 samples in total with more than 10,000 categories, covering various types such as human faces, animals, clothes, human-made tools, etc. Each sample consists of a reference image, a segmentation mask of the object of interest in the reference image, a target image, and a text caption of the target image. The reference image with its segmentation mask provides ID information, the text caption of the target image offers semantic-level guidance for generation, and the target image serves as the ground truth.
Codes and dataset will be released soon!
If you find AnyMaker useful for your research and applications, please cite using this BibTeX:
@article{kong2024anymaker,
title={AnyMaker: Zero-shot General Object Customization via Decoupled Dual-Level ID Injection},
author={Kong, Lingjie and Wu, Kai and Hu, Xiaobin and Han, Wenhui and Peng, Jinlong and Xu, Chengming and Luo, Donghao and Zhang, Jiangning and Wang, Chengjie and Fu, Yanwei},
journal={arXiv preprint arXiv:2406.11643},
year={2024}
}